12-factor-agents README

The project's README from github.com/humanlayer/12-factor-agents. As one markdown file: README.md.

12-Factor Agents - Principles for building reliable LLM applications

Code License: Apache 2.0 Content License: CC BY-SA 4.0 Discord Server YouTube Deep Dive YouTube Deep Dive

In the spirit of 12 Factor Apps. The source for this project is public at https://github.com/humanlayer/12-factor-agents, and I welcome your feedback and contributions. Let's figure this out together!

[!TIP] Missed the AI Engineer World's Fair? Catch the talk here

Looking for Context Engineering? Jump straight to factor 3

Want to contribute to npx/uvx create-12-factor-agent - check out the discussion thread

Screenshot 2025-04-03 at 2 49 07 PM

Hi, I'm Dex. I've been hacking on AI agents for a while.

I've tried every agent framework out there, from the plug-and-play crew/langchains to the "minimalist" smolagents of the world to the "production grade" langraph, griptape, etc.

I've talked to a lot of really strong founders, in and out of YC, who are all building really impressive things with AI. Most of them are rolling the stack themselves. I don't see a lot of frameworks in production customer-facing agents.

I've been surprised to find that most of the products out there billing themselves as "AI Agents" are not all that agentic. A lot of them are mostly deterministic code, with LLM steps sprinkled in at just the right points to make the experience truly magical.

Agents, at least the good ones, don't follow the "here's your prompt, here's a bag of tools, loop until you hit the goal" pattern. Rather, they are comprised of mostly just software.

So, I set out to answer:

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

Welcome to 12-factor agents. As every Chicago mayor since Daley has consistently plastered all over the city's major airports, we're glad you're here.

Special thanks to @iantbutler01, @tnm, @hellovai, @stantonk, @balanceiskey, @AdjectiveAllison, @pfbyjy, @a-churchill, and the SF MLOps community for early feedback on this guide.

The Short Version: The 12 Factors

Even if LLMs continue to get exponentially more powerful, there will be core engineering techniques that make LLM-powered software more reliable, more scalable, and easier to maintain.

Visual Nav
factor 1 factor 2 factor 3
factor 4 factor 5 factor 6
factor 7 factor 8 factor 9
factor 10 factor 11 factor 12

How we got here

For a deeper dive on my agent journey and what led us here, check out A Brief History of Software - a quick summary here:

The promise of agents

We're gonna talk a lot about Directed Graphs (DGs) and their Acyclic friends, DAGs. I'll start by pointing out that...well...software is a directed graph. There's a reason we used to represent programs as flow charts.

010-software-dag

From code to DAGs

Around 20 years ago, we started to see DAG orchestrators become popular. We're talking classics like Airflow, Prefect, some predecessors, and some newer ones like (dagster, inggest, windmill). These followed the same graph pattern, with the added benefit of observability, modularity, retries, administration, etc.

015-dag-orchestrators

The promise of agents

I'm not the first person to say this, but my biggest takeaway when I started learning about agents, was that you get to throw the DAG away. Instead of software engineers coding each step and edge case, you can give the agent a goal and a set of transitions:

025-agent-dag

And let the LLM make decisions in real time to figure out the path

026-agent-dag-lines

The promise here is that you write less software, you just give the LLM the "edges" of the graph and let it figure out the nodes. You can recover from errors, you can write less code, and you may find that LLMs find novel solutions to problems.

Agents as loops

As we'll see later, it turns out this doesn't quite work.

Let's dive one step deeper - with agents you've got this loop consisting of 3 steps:

  1. LLM determines the next step in the workflow, outputting structured json ("tool calling")
  2. Deterministic code executes the tool call
  3. The result is appended to the context window
  4. Repeat until the next step is determined to be "done"
initial_event = {"message": "..."}
context = [initial_event]
while True:
  next_step = await llm.determine_next_step(context)
  context.append(next_step)

  if (next_step.intent === "done"):
    return next_step.final_answer

  result = await execute_step(next_step)
  context.append(result)

Our initial context is just the starting event (maybe a user message, maybe a cron fired, maybe a webhook, etc), and we ask the llm to choose the next step (tool) or to determine that we're done.

Here's a multi-step example:

027-agent-loop-animation

GIF Version

027-agent-loop-animation

Why 12-factor agents?

At the end of the day, this approach just doesn't work as well as we want it to.

In building HumanLayer, I've talked to at least 100 SaaS builders (mostly technical founders) looking to make their existing product more agentic. The journey usually goes something like:

  1. Decide you want to build an agent
  2. Product design, UX mapping, what problems to solve
  3. Want to move fast, so grab $FRAMEWORK and get to building
  4. Get to 70-80% quality bar
  5. Realize that 80% isn't good enough for most customer-facing features
  6. Realize that getting past 80% requires reverse-engineering the framework, prompts, flow, etc.
  7. Start over from scratch
Random Disclaimers

DISCLAIMER: I'm not sure the exact right place to say this, but here seems as good as any: this in BY NO MEANS meant to be a dig on either the many frameworks out there, or the pretty dang smart people who work on them. They enable incredible things and have accelerated the AI ecosystem.

I hope that one outcome of this post is that agent framework builders can learn from the journeys of myself and others, and make frameworks even better.

Especially for builders who want to move fast but need deep control.

DISCLAIMER 2: I'm not going to talk about MCP. I'm sure you can see where it fits in.

DISCLAIMER 3: I'm using mostly typescript, for reasons but all this stuff works in python or any other language you prefer.

Anyways back to the thing...

Design Patterns for great LLM applications

After digging through hundreds of AI libriaries and working with dozens of founders, my instinct is this:

  1. There are some core things that make agents great
  2. Going all in on a framework and building what is essentially a greenfield rewrite may be counter-productive
  3. There are some core principles that make agents great, and you will get most/all of them if you pull in a framework
  4. BUT, the fastest way I've seen for builders to get high-quality AI software in the hands of customers is to take small, modular concepts from agent building, and incorporate them into their existing product
  5. These modular concepts from agents can be defined and applied by most skilled software engineers, even if they don't have an AI background

The fastest way I've seen for builders to get good AI software in the hands of customers is to take small, modular concepts from agent building, and incorporate them into their existing product

The 12 Factors (again)

Honorable Mentions / other advice

Contributors

Thanks to everyone who has contributed to 12-factor agents!

dexhorthy Sypherd tofaramususa a-churchill Elijas hugolmn jeremypeters

kndl maciejkos pfbyjy 0xRaduan zyuanlim lombardo-chcg sahanatvessel

License

All content and images are licensed under a CC BY-SA 4.0 License

Code is licensed under the Apache 2.0 License

12-factor-agents (humanlayer/12-factor-agents)

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12-factor-agents
What is this?A written guide of 12 (plus a bonus 13th) engineering principles for building LLM-powered agent software reliable enough for production customers, distilled from the author's experience at HumanLayer and conversations with many SaaS founders.
Is this a code framework I install, or just a guide to read?It is a documentation/essay repository, not an installable framework or library; the principles are illustrated with TypeScript code snippets but are meant to be applied inside your own stack rather than adopted as a dependency.
How is the guide organized and where should I start?It walks through 12 numbered factors (e.g. own your prompts, own your context window, contact humans with tool calls) plus an honorable-mention Factor 13, each as its own linked section with explanation and examples; start at Factor 1: Natural Language to Tool Calls and read in order.
Do I need any agent framework (LangChain, CrewAI, etc.) to use these ideas?No, the whole point is that you don't need to adopt a framework; the author argues most production-grade agentic products are mostly deterministic code with LLM calls sprinkled in, and the principles can be applied directly to an existing codebase.
Who is this guide written for?Technical founders and software engineers who are trying to make an existing product more agentic and want to get past the common 70-80% quality ceiling that generic agent frameworks hit.
What is Factor 13, the bonus principle?Pre-fetch all the context you might need, an honorable-mention factor added after the original 12.
Why did the author write this instead of just using an existing agent framework?After trying nearly every framework (crew/langchain, smolagents, langgraph, griptape) and talking to many founders, the author found most production customer-facing agents barely use frameworks at all, so he distilled the small set of engineering ideas that actually make them reliable.
Does this guide cover MCP (Model Context Protocol)?No, the author explicitly says he is not going to talk about MCP in this guide.
What related project did the same methodology lead to?The author's team built open-source agents under the got-agents/agents methodology and also a framework for running distributed agents in Kubernetes.
What are similar projects?ai-system-design, agentscope, PocketFlow, Prompt-Engineering-Guide, advanced-context-engineering-for-coding-agents, Agent-Learning-Hub, adk-python, haystack
Which topics is it in?context-window, software-architecture, system-design, llms, agent-framework, best-practices, memory, large-language-models, orchestration, prompt-engineering, rag, agentic-ai
Creator
humanlayer
Size
44.4 MB
Stars
26.7K
Updated
CommitSep 21
Age
1y
Commits · 90 Days